Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

TL;DR

This paper introduces generalized exponential random graph models that enable statistical analysis of networks with valued edges, expanding the applicability of ERGMs to a broader range of relational data.

Contribution

It develops a new class of ERGMs capable of modeling valued-edge networks, overcoming previous limitations of binary-edge models.

Findings

Enables analysis of networks with weighted edges

Expands ERGM applicability to diverse relational data

Provides a flexible modeling framework for valued networks

Abstract

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis.